QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data

Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high spatial resolution, a quantitative irradiance estimation network, named QIENet, is proposed. Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively. Not only remote sensing data, but also GHI-related time information (hour, day, and month) and geographical information (altitude, longitude, and latitude), are used as the inputs of QIENet. The satellite spectral channels B07 and B11 - B15 and time are recommended as model inputs for QIENet according to the spatial distributions of annual solar energy. Meanwhile, QIENet is able to capture the impact of various clouds on hourly GHI estimates. More importantly, QIENet does not overestimate ground observations and can also reduce RMSE by 27.51%/18.00%, increase R2 by 20.17%/9.42%, and increase r by 8.69%/3.54% compared with ERA5/NSRDB. Furthermore, QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02{\deg} * 0.02{\deg}(approximately 2km * 2km) for many applied energy fields

QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data

Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high spatial resolution, a quantitative irradiance estimation network, named QIENet, is proposed. Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively. Not only remote sensing data, but also GHI-related time information (hour, day, and month) and geographical information (altitude, longitude, and latitude), are used as the inputs of QIENet. The satellite spectral channels B07 and B11–B15 and time are recommended as model inputs for QIENet according to the spatial distributions of annual solar energy. Meanwhile, QIENet is able to capture the impact of various clouds on hourly GHI estimates. More importantly, QIENet does not overestimate ground observations and can also reduce RMSE by 27.51%/18.00%, increase R2 by 20.17%/9.42%, and increase r by 8.69%/3.54% compared with ERA5/NSRDB. Furthermore, QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02°×0.02° (approximately 2km×2km) for many applied energy fields

Water Deficit Caused by Land Use Changes and Its Implications on the Ecological Protection of the Endorheic Dalinor Lake Watershed in Inner Mongolia, China

Dalinor Lake, the second-largest endorheic salt lake in Inner Mongolia, has shown a shrinking trend given the lack of a significant decrease in precipitation (PRE). Based on high-spatial-resolution datasets, we employed a linear regression model, Theil–Sen median trend analysis, the Mann–Kendall test, and a land use transfer matrix to identify the spatio-temporal distribution and trends of PRE and actual evapotranspiration (AET) at the watershed scale during 2001–2019; then, the water deficit (WD) caused by land use changes in different surface lithology zones was analyzed. The results showed that the annual PRE and WD of the Dalinor Lake watershed showed insignificant upward trends, while the annual AET showed a significant upward trend. Spatially, about 89% of the watershed showed a significant upward trend for AET, while 12% showed a weak significant upward trend for PRE. The WDs of the aeolian sand zone and the sand, gravel, and silt accumulation zone were most heavily affected by the new increased land use from 2001 to 2019, accounting for 43.14% and 25.56% of the total WD of the watershed, respectively. Specifically, the WD of the aeolian sand zone caused by the new increased grassland and farmland in 2019 accounted for 41.92% and 18.52% of the total WD of the zone, respectively. The WD of the sand, gravel, and silt accumulation zone caused by the new increased grassland and farmland in 2019 accounted for 37.07% and 35.59% of the total WD of the zone, respectively. The WD caused by the new increased land use was increased by 7.78 million m3 in 2019 compared with the corresponding land use type in 2001, which would decrease the water yield. It is necessary to strengthen the protection of regional forest ecosystems in the granite and terrigenous clastic rock zone; standardize pasture management and reduce farmland reclamation in the sand, gravel, and silt accumulation zone, the aeolian sand zone, and the basalt platform zone; and reduce unnecessary impervious land construction in the aeolian sand zone

A Theoretical Model of Roof Self-Stability in Solid Backfilling Mining and Its Engineering Verification

Roof self-stability in backfilling mining was proposed to explore its connotation and characteristics after a comparative analysis of roof structures under long-wall caving and backfilling mining. The mechanical analysis models of roof self-stability along strike and dip were established. After that, the mechanical equations for cooperative roof control were constructed to analyze the elastic foundation coefficients of the backfill, support peak load, unsupported-roof distance, and drilling effect of the working face along strike, the size of the working face, and the section pillar effect along dip. Research showed that the roof self-stability was greatly impacted by the elastic foundation coefficient of backfill, and it was less impacted by the support peak load along strike. The unsupported-roof distance had no obvious effect on roof self-stability. Roof self-stability was significantly affected by the working face and coal-pillar length along the dip. Therefore, the engineering control method of roof self-stability was proposed. The backfilling engineering practice in Xinjulong Coal Mine showed that the maximum roof subsidence was 438 mm, and the backfill ratio was 86.3% when the supporting intensity of backfilling hydraulic support was 0.94 MPa; the advanced distance of the working face was greater than 638 m; the foundation coefficient of backfilling material was 4.16 × 108 Nm−3. The roof formed the self-stability structure, which satisfied safe coal mining under buildings, water bodies, and railways

A Theoretical Model of Roof Self-Stability in Solid Backfilling Mining and Its Engineering Verification

Roof self-stability in backfilling mining was proposed to explore its connotation and characteristics after a comparative analysis of roof structures under long-wall caving and backfilling mining. The mechanical analysis models of roof self-stability along strike and dip were established. After that, the mechanical equations for cooperative roof control were constructed to analyze the elastic foundation coefficients of the backfill, support peak load, unsupported-roof distance, and drilling effect of the working face along strike, the size of the working face, and the section pillar effect along dip. Research showed that the roof self-stability was greatly impacted by the elastic foundation coefficient of backfill, and it was less impacted by the support peak load along strike. The unsupported-roof distance had no obvious effect on roof self-stability. Roof self-stability was significantly affected by the working face and coal-pillar length along the dip. Therefore, the engineering control method of roof self-stability was proposed. The backfilling engineering practice in Xinjulong Coal Mine showed that the maximum roof subsidence was 438 mm, and the backfill ratio was 86.3% when the supporting intensity of backfilling hydraulic support was 0.94 MPa; the advanced distance of the working face was greater than 638 m; the foundation coefficient of backfilling material was 4.16 × 108 Nm−3. The roof formed the self-stability structure, which satisfied safe coal mining under buildings, water bodies, and railways

Aromatic hydrocarbons as ozone precursors before and after outbreak of the 2008 financial crisis in the Pearl River Delta region, south China

In the second half of 2008 China's highly industrialized Pearl River Delta (PRD) region was hard-hit by the financial crisis (FC). This study reports volatile organic compounds measured in the PRD during November–December in both 2007 before the FC and 2008 after the FC. While total mixing ratios of non-methane hydrocarbons (NMHCs) on average were only about 7% lower from 40.2 ppbv in 2007 to 37.5 ppbv in 2008, their ozone formation potentials (OFPs) dropped about 30%, resulting from about 55% plummet of aromatic hydrocarbons (AHs) against a greater than 20% increase of total alkanes/alkenes. The elevated alkanes and alkenes in 2008 could be explained by greater emissions from vehicle exhausts and LPG combustion due to rapid increase of vehicle numbers and LPG consumption; the drop of AHs could be explained by reduced emissions from industries using AH-containing solvents due to the influence of the FC, as indicated by much lower ratios of toluene to benzene and of xylenes/ trichloroethylene/tetrachloroethylene to carbon monoxide (CO) in 2008. Source apportionment by positive matrix factorization (PMF) also revealed much less contribution of industry solvents to total anthropogenic NMHCs and particularly to toluene and xylenes in 2008 than in 2007. Based on PMF reconstructed source contributions, calculated OFPs by industrial emissions were responsible for 40.8% in 2007 in contrast to 18.4% in 2008. Further investigation into local industry output statistics suggested that the plummet of AHs in 2008 should be attributed to small enterprises, which contributed largely to ambient AHs due to their huge numbers and non-existent emission treatment, but were much more influenced by the FC.Department of Civil and Environmental EngineeringAuthor name used in this publication: Chan, LoyinAuthor name used in this publication: Frank S. C. Le